bioRxivEcologyPreprint
Benefit Transfer Loops Turn Cheating into a Scaffold for Microbial Diversity
Jiqi ShaoiD, Yinxiang LiiD, Shaohua GuiD, Xiaoyi ZhangiD, Shaopeng WangiD, Xueming LiuiD, Zhiyuan LiiD
Abstract
Niche construction drives ecological dynamics, yet the tragedy of the commons predicts that non-contributing cheaters will undermine cooperation. Here, we studied microbial iron competition by combining dynamic modeling with benefit flow graphs, demonstrating that moderate cheating is not merely tolerated but essential for diversity. In small communities, mutual exploitation forms closed loops enabling steady or dynamic coexistence. In larger communities, we uncovered a paradox: increasing cheating breadth promotes community-level extinction, yet fosters higher biodiversity in surviving communities. We resolve this paradox by mapping ecological dynamics onto the topology of the "Maximal Benefit Transfer Graph", which predicts community fate through its core structure. Broad cheating eliminates the self-loop core that drives competitive exclusion, but increases "terminator" sinks that cause collapse. However, when communities avoid these sinks, cheating aggregates the network and generates cyclic loops to enable coexistence. Thus, structured exploitation acts not as destabilizing vulnerability but as necessary architecture for biodiversity. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/568182v3_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@143f144org.highwire.dtl.DTLVardef@194c2adorg.highwire.dtl.DTLVardef@1792d4aorg.highwire.dtl.DTLVardef@8c96a7_HPS_FORMAT_FIGEXP M_FIG C_FIG How does cheating affect microbial biodiversity? By mapping the strongest benefit flows between species, we discovered a topological rule for survival. While too much cheating creates dead-ends that crash the system, moderate cheating connects species into self-sustaining loops. These "exploitation cycles" act as a scaffold, supporting high diversity and complex population.
- research focus: ecology & biodiversity
- research focus: cell-cell communication
- research focus: clustering
- approach: deep learning
- approach: statistical model
- Citations
- 4
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